Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI)

ABSTRACT

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

KEYWORDS:

• Analysis Ready Data
• Artificial General Intelligence
• Artificial Narrow Intelligence
• big data
• cognitive science
• computer vision
• Earth observation
• essential climate variables
• Global Earth Observation System of (component) Systems
• inductive/ deductive/ hybrid inference
• Scene Classification Map
• Space Economy 4.0
• radiometric corrections of optical imagery from atmospheric, topographic, adjacency and bidirectional reflectance distribution function effects
• semantic content-based image retrieval
• 2D spatial topology-preserving/retinotopic image mapping
• world ontology (synonym for conceptual/ mental/ perceptual model of the world)

Acknowledgments

Andrea Baraldi thanks Prof. Raphael Capurro for his hospitality, patience, politeness and open-mindedness. The authors wish to thank the editors and reviewers for their competence, patience and willingness to help.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Supplemental data

Supplemental data for this article can be accessed here.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported in part by the Austrian Research Promotion Agency (FFG), under the: (i) ASAP 16 project call, project title: SemantiX - A cross-sensor semantic EO data cube to open and leverage essential climate variables with scientists and the public, Grant ID: 878939, and (ii) ASAP 17 project call, project title: SIMS - Soil sealing identification and monitoring system, Grant ID: 885365.

Notes on contributors

Andrea Baraldi

Andrea Baraldi received his Laurea (M.S.) degree in Electronic Engineering from the University of Bologna, Italy, in 1989, a Master Degree in Software Engineering from the University of Padua, Italy, in 1994, and a PhD degree in Agricultural and Food Sciences from the University of Naples Federico II, Italy, in 2017. He has held research positions at the Italian Space Agency (ASI), Rome, Italy (2018-2021), Dept. of Geoinformatics (Z-GIS), Univ. of Salzburg, Austria (2014-2017), Dept. of Geographical Sciences, University of Maryland (UMD), College Park, MD (2010-2013), European Commission Joint Research Centre (EC-JRC), Ispra, Italy (2000-2002; 2005-2009), International Computer Science Institute (ICSI), Berkeley, CA (1997-1999), European Space Agency Research Institute (ESRIN), Frascati, Italy (1991-1993), Italian National Research Council (CNR), Bologna, Italy (1989, 1994-1996, 2003-2004). In 2009, he founded Baraldi Consultancy in Remote Sensing, a one-man company located in Modena, Italy. In Feb. 2014, he was appointed with a Senior Scientist Fellowship at the German Aerospace Center (DLR), Oberpfaffenhofen, Germany. In Feb. 2015, he was a visiting scientist at the Ben Gurion Univ. of the Negev, Sde Boker, Israel, funded by the European Commission FP7 Experimentation in Ecosystem Research (ExpeER) project. His main research interests center on image pre-processing and understanding, with special emphasis on the research and development of automatic near real-time Earth observation spaceborne/airborne image understanding systems in operational mode, consistent with human visual perception. Dr. Baraldi’s awards include the Copernicus Masters Prize Austria 2020, Copernicus Masters - Winner 2015 of the T-Systems Big Data Challenge and the 2nd-place award at the 2015 IEEE GRSS Data Fusion Contest. He served as Associate Editor of the IEEE Trans. Neural Networks journal from 2001 to 2006.

Luca D. Sapia

Luca D. Sapia received his Master’s Degree in Civil Engineering from the University of Bologna, Italy, in 2015. He has held research positions at Arpae Emilia-Romagna, Bologna, Italy (2015-2020), Department of Civil, Chemical, Environmental, and Materials Engineering (DICAM), University of Bologna, Bologna, Italy (2018-2019), Interdepartmental Center for Energy and the Environment (CIDEA), University of Parma, Parma, Italy (2016-2018). From 2019 to 2021, he worked for the European Space Agency (ESA) at Serco Italy as science support specialist and Earth Observation (EO) products analysis expert for the Copernicus data Quality Control (CQC) service. Currently, he is Program Manager of the Earth Observation Applications Unit at CGI Italy. His main interests center on EO data acquisition and systematic generation of EO data-derived value-adding information products and services. In the last years, he focused on developing, validating and transferring EO technologies to the Italian agricultural market. He is the inventor of the “LET” (Landsat EvapoTranspiration) operational service for the detection of unauthorized water withdrawals for irrigation use in agriculture.

Dirk Tiede

Dirk Tiede, PhD, is Associate Professor at the University of Salzburg, Department of Geoinformatics – Z_GIS, Austria, and head of the research area EO Analytics. His research focuses on methodological developments in image analysis using optical EO data, object-based methodologies and process automation in the context of Big EO data analysis. Research fields include environmental monitoring and support of humanitarian relief operations, for which he received the Christian-Doppler-Award of the Federal State of Salzburg in 2014, the Copernicus Master Award “Big Data Challenge” in 2015, the Copernicus Prize Austria in 2020 and was ranked 2nd in the IEEE GRSS Data Fusion Contest 2015.

Martin Sudmanns

Martin Sudmanns, PhD, is postdoctoral researcher at the University of Salzburg, Department of Geoinformatics – Z_GIS, Austria with a research focus on Geoinformatics, computer-based representation of natural phenomena in spatial data models, spatio-temporal Earth observation analysis in the context of data cubes and big EO data. He received the Copernicus Master Award “Big Data Challenge” in 2015 and the Copernicus Prize Austria in 2020.

Hannah L. Augustin

Hannah L. Augustin, MSc, is a PhD researcher in Earth observation (EO) Analytics at the University of Salzburg, Department of Geoinformatics – Z_GIS, Austria with a research focus on semantic EO data cubes, automated and transferable processes for generating EO-informed indicators from big optical EO imagery and related geovisualisation. She was part of the team awarded with the Copernicus Prize Austria in 2020.

Stefan Lang

Stefan Lang, PhD, Geographer, GIS and Remote Sensing specialist and Associate Professor at the University of Salzburg, Research Coordinator at Z_GIS and co-head of the Earth Observation division. He is leading the Christian-Doppler Laboratory for geo-humanitarian action, with a research focus on OBIA, hybrid AI, systems thinking, data assimilation, multi-scale regionalisation, validation.